Abstract

The information-transfer rate (ITR) is commonly used to assess the performance of brain-computer interfaces (BCIs). Various studies have shown that the optimal number of mental tasks to be used is fairly low, around 3 or 4. We propose an experimental validation as well as a formal approach to demonstrate and confirm that this optimum is user and BCI design dependent. Even if increasing the number of mental tasks to the optimum indeed leads to an increase of the ITR, the gain remains small. This might not justify the added complexity in terms of protocol design.

This paper describes the work being conducted in the domain of brain-computer interaction (BCI) at the Multimodal Interaction Group, Computer Vision and Multimedia Laboratory, University of Geneva, Geneva, Switzerland. The application focus of this work is on multimodal interaction rather than on rehabilitation, that is how to augment classical interaction by means of physiological measurements. Three main research topics are addressed. The first one concerns the more general problem of brain source activity recognition from EEGs. In contrast with classical deterministic approaches, we studied iterative robust stochastic based reconstruction procedures modeling source and noise statistics, to overcome known limitations of current techniques. We also developed procedures for optimal electroencephalogram (EEG) sensor system design in terms of placement and number of electrodes. The second topic is the study of BCI protocols and performance from an information-theoretic point of view. Various information rate measurements have been compared for assessing BCI abilities. The third research topic concerns the use of EEG and other physiological signals for assessing a user's emotional status.

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